Dear Kyubyong,
great work - thank you very much for proving these word vectors!
One question: Which model did you use to train your word vectors with word2vec? Skip-gram or cbow? Is this the standard model as reported in Mikolov et al. (2013) or a modified variant?
And which parameters did you use to train the model for each language? Always the default parameters in make_wordvectors.sh?
Given make_wordvectors.sh and make_wordvectors.py, it seems that @Kyubyong used the gensim implementation of word2vec. Thus, by default, I believe he chose CBOW model. see gensim doc
Dear Kyubyong, great work - thank you very much for proving these word vectors! One question: Which model did you use to train your word vectors with word2vec? Skip-gram or cbow? Is this the standard model as reported in Mikolov et al. (2013) or a modified variant? And which parameters did you use to train the model for each language? Always the default parameters in make_wordvectors.sh?